Hand and face landmark detection in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding landmark detection outputs

When a hand landmark detection model processes an image, what does its output typically represent?

A Coordinates of key points on the hand such as fingertips and joints

B A binary mask highlighting the hand region

C A classification label indicating hand gestures

D The raw pixel values of the hand area

💡 Hint

Think about what 'landmark' means in this context.

Landmark detection models output coordinates of specific points on the hand or face, like fingertips or facial features, to understand their positions.

❓ Predict Output

intermediate

Output shape of face landmark detection model

Given a face landmark detection model that detects 468 points per face, what is the shape of the output tensor for a batch of 5 images?

Computer Vision

batch_size = 5
num_landmarks = 468
output_shape = (batch_size, num_landmarks, 3)  # x, y, z coordinates

A (5, 3, 468)

B (5, 468, 3)

C (3, 468, 5)

D (468, 5, 3)

💡 Hint

Consider batch size first, then landmarks, then coordinates.

The output tensor shape is (batch_size, number_of_landmarks, coordinates_per_point). For 5 images and 468 landmarks with 3D coordinates, it is (5, 468, 3).

❓ Model Choice

advanced

Choosing a model for real-time hand landmark detection

You want to build a mobile app that detects hand landmarks in real-time video. Which model architecture is best suited?

A A deep transformer model with self-attention layers

B A large ResNet-based model with many layers for high accuracy

C A lightweight MobileNet-based model optimized for speed

D A simple linear regression model

💡 Hint

Think about balancing speed and accuracy on mobile devices.

MobileNet-based models are designed to be lightweight and fast, making them ideal for real-time applications on mobile devices.

❓ Metrics

advanced

Evaluating landmark detection accuracy

Which metric best measures the accuracy of predicted hand landmarks compared to ground truth points?

A Precision and recall of hand detection bounding boxes

B Classification accuracy of hand gesture labels

C Confusion matrix of detected vs missed landmarks

D Mean Squared Error (MSE) between predicted and true landmark coordinates

💡 Hint

Focus on how close predicted points are to actual points.

MSE measures the average squared distance between predicted and true landmark coordinates, quantifying prediction accuracy.

🔧 Debug

expert

Debugging inconsistent landmark predictions

You notice that your face landmark detection model sometimes predicts landmarks outside the face region. What is the most likely cause?

A The input images are not normalized or preprocessed consistently

B The model architecture is too deep

C The optimizer learning rate is too low

D The batch size during training was too large

💡 Hint

Think about input data quality and consistency.

If input images are not normalized or preprocessed consistently, the model may receive unexpected inputs causing incorrect landmark predictions outside the face.